X chromosome inactivation (XCI) occurs in females early in development, where one of the two X chromosomes is transcriptionally silenced via DNA methylation to equalize X-linked gene expression with males. XCI results in epigenomic variability unique to females, where the identity of the inactive X varies from tissue to tissue and cell to cell, but some of the genes on the X chromosome actually escape inactivation (?escapee genes?) and both alleles are transcribed. Because the X chromosome contains an excess of sex and reproductive-related genes, XCI aberrations may impact the incidence and progression of female-specific cancers, where onset is often during the peri- and post-menopausal period. In fact, the inactive X has been reported to be lost in ovarian cancer, and the process of XCI has been shown to be non-random in lymphocytes of ovarian cancer patients, particularly in BRCA mutation carriers. However, the chromosome- wide and gene-level patterns in both ovarian tumors and unaffected tissue are unknown. Furthermore, previous analysis approaches have not effectively integrated the multiple relevant molecular data types necessary to resolve XCI ?escapee? status. The use of novel, innovative methods to integrate genomic, methylomic, and transcriptomic data are required to comprehensively characterize XCI profiles and evaluate their role in disease. This project fills a critical knowledge gap about XCI and ovarian cancer by using a comprehensive multi-omic data integration approach, including DNA methylation, genotype, RNA expression, and structural variant data. Our overarching hypothesis is that abnormal disruption of XCI, such as loss of XCI or inactivation at unexpected loci, promotes tumorigenesis through re-activation of oncogenes or de-activation of tumor suppressor genes. We will investigate this hypothesis by utilizing a unique resource of samples from ovarian cancer patients with rich genomic data, combined with data from unaffected ovarian tissue. We will characterize chromosome-wide XCI patterns and identify genes that undergo and escape XCI first in normal ovarian tissue. We will then identify genes that differentially escape XCI in ovarian tumors compared to normal ovarian tissue, and determine if chromosome-wide XCI patterns in ovarian tumors are associated with clinical factors. The role of XCI is an understudied area in cancer research, and this project represents the most comprehensive characterization of XCI in ovarian tissue to date. We will better understand tumors at these sites and establish the extent to which XCI patterns are variable and clinically relevant, and inform future studies to examine XCI in tumorigenesis and disease outcome. Notably, the ability to determine a patient?s XCI profile may lead to advances in individualized medicine if associations with clinical features and outcomes are established. Furthermore, the integrative XCI methods developed here will have broader utility to examine other female-specific cancers or cancers that exhibit sex differences in incidence, progression, or outcomes.
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